Hi, thank you for your instructions. Now that I have removed the instances with a particular missing attribute value, I would like to train with the remaining data to predict the missing values I just removed, how do I do that?

I want to predict an attribute which is present in training dataset but absent in test dataset. Even if it is present, I tried using that brand instances as Null or missing values. I am using randomforest and it gats wrapped in inputmapped classifier and although it executes but it predicts only ? marks(i.e,. Null).

the dataset that I am using contains importer names,unit,currency,duty values,brand specs(in text,some in numeric or real). The value that I am trying to predict is a text(nominal). Most of the text data can be easily lablled as binary data type.

Nice article, however, I feel like you missed a good point of explaining which step on missing data should be taken and so I am a tad confused.

At what point would I prefer to remove samples that contain missing information over the choice of Imputing them?

The removal of samples also leads me to the question of what is the minimum threshold of samples that you should retain in your dataset for it to still be representative of the objective you are attempting to carry out?

Hello, in case of nominal attribute, how could I remove missing instances (e.g. flagged with “?”)? I didn’t find a filter. Applying the “Remove with values” should be able but it didn’t work. Thank you.

I wanna ask about missing data. I know sometimes missing data are too important for us to completely remove them from analysis. For instance I have a data set that has 30% of he overall data missing. I am trying to do a time series prediction and data are missing consistently for Saturdasy and Sundays for 5 years. I am employing a Kalman smoothing method to fill in the spaces for those two days…

Do you think this method may be relatively okay? or do you think I should remove the rows with missing values?

could you please mention a few evaluation (if any) techniques to check how well my method of replacing missing data worked? How do I know which is the best method since there’s no way I could ever know what this missing data are.

hi jason i am doing prediction with naive bayes and i have a lot of missing values in my dataset ,for this moment i use replace missing values and then pre-process and predict is it mayby better if i delete the attributes with 95% of missing values?